The present disclosure relates generally to supply chain performance management, and in particular, to a method for supporting a common performance management process.
In today's global marketplace, business units within an enterprise typically deal with multiple supply chain facilities spread out over a wide geographic region. Supply chain facilities may be associated with any parties involved in the supply chain process such as suppliers, cross-docks, plants, distribution centers and dealers. Optimally, these enterprises prefer to maintain business relationships with external supply chain facilities that have consistently demonstrated a high degree of competence in terms of their abilities, for example, in satisfying customer orders and in providing timely deliveries. However, effectively managing the performance of supply chain facilities has not been an easy task due to lack of common performance metrics and performance analysis tools. Effective management not only requires the capability of tracking the performance but requires providing recommendations and/or strategies for performance improvement.
A variety of methods have been used to measure and/or analyze the performance of supply chain facilities, including bulk metrics, warehouse performance analysis, regression analysis, productivity ratios, parametric analysis, and data envelope analysis (DEA). Bulk metrics is a conventional method based on a single performance metric, such as freight volume (or weight), operating cost, response time or shipping accuracy, etc. However, these crude metrics do not reflect the real performance of the system which is affected by many other input and output factors including the level of investment in operational resources (such as material handling equipment, information technology, and personnel), facility design and location, and the different types of services provided.
Warehouse performance analysis is a graphical approach based on the uses of people, space, and systems. It visually demonstrates the discrepancy between current and world-class performance; however, it cannot provide a single quantitative measure of performance.
Productivity ratio measures performance based on the ratio of a single-output over a single-input. While parametric analysis does apply to a multiple inputs and outputs setting, an explicit functional form and a set of weights need to be given a priori, for the production function. Similar to regression analysis, a regression function has to be pre-specified. In addition, the standard regression model only derives an average level of a single output from a given bundle of inputs, not the maximum achievable outcome. Therefore, it is not appropriate to be used for performance analysis.
DEA is recognized as a non-parametric, optimization-based method for measuring performance. It has multiple advantages over the aforementioned methods in that (1) it derives a quantitative measure based on both input and output factors, (2) it handles multiple inputs and outputs, (3) it relies only on sampled data, there is no need for a priori information regarding the functional form and which inputs and outputs are most important, and (4) it provides relative efficiency ranking based on linear programming optimization.
The process, inputs and outputs of performance analysis can vary greatly for different supply chain facilities within a single enterprise. It can be difficult to get an entire enterprise to utilize a common tool for supply chain facility performance analysis. Such tools are typically difficult to adapt to differing types of supply chain facilities with different kinds of performance criteria. This lack of adaptability can make the tool difficult to use and can lead to a lack of use by the business units within the enterprise. As a result, business units continue to rely on their locally developed methods of supply chain performance management and performance analysis.
What is needed is a common, enterprise wide tool for supporting supply chain facility performance analysis. The tool should be flexible enough to allow for process variations for different types of supply chain partners and for different business unit requirements, while still retaining a common core analysis process that is used to support performance management for all supply chain facilities utilized by the enterprise. The tool should support a process of analyzing supply chain facilities performance in an adaptive and comprehensive manner. In addition, the tool should support the creation of improvement prescriptions for supply chain facilities based on the results of the analysis.